A number of techniques have been proposed to enable extraction of the foreground from a scene, for example, the extraction of a person from a digital image showing the person standing in front of a scenic view. This process of splitting an image into the foreground and background is known as image segmentation. Image segmentation comprises labeling Image elements (such as pixels, groups of pixels, voxels or groups of voxels) as either a foreground or a background image element. This is useful in digital photography, medical image analysis, and other application domains where it is helpful to find a boundary between an object in the image and a background. The object and the background may then be processed separately, differently, etc. In the case of a medical image it may be appropriate to segment out a region of an image depicting a tumor or organ such as the lungs in order to enable a surgeon to interpret the image data.
As digital camera and other image acquisition technology develops, however, the resolution of the digital images being captured is increasing rapidly and as a result the size of image files is also increasing rapidly. Images of 10-20 MPixels are now not uncommon and many mobile phones contain cameras capable of capturing images of five MPixels or more. Medical imaging systems can acquire 3D volumes with billions of voxels. In addition to requiring larger storage units (both in the digital camera and for off-camera storage), these larger image file sizes require significantly more processing to achieve image segmentation. If known image segmentation techniques are applied to such high-resolution images, the process can be very slow and a user may experience unacceptable delays.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image segmentation techniques.